In this study, we describe the use of back-channelling patterns extracted from a dialogue corpus as a mean to characterising text-based dialogue systems. Our goal was to provide system users with the feeling that they are interacting with distinct individuals rather than artificially created characters. An analysis of the corpus revealed that substantial difference exists among speakers regarding the usage patterns of back-channelling. The patterns consist of back-channelling frequency, types, and expressions. They were used for system characterisation. Implemented system characters were tested by asking users of the dialogue system to identify the source speakers in the corpus. Experimental results suggest that possibility of using back-channelling patterns alone to characterize the dialogue system in some cases even among the same age and gender groups.

We investigate an automatic dialogue segmentation method using both verbal and non-verbal modalities. Dialogue contents are used for the initial segmentation of dialogue; then, gesture occurrences are used to remove the incorrect segment boundaries. A unique characteristic of our method is to use verbal and non-verbal information separately. We use a three-party dialogue that is rich in gesture as data. The transcription of the dialogue is segmented into topics without prior training by using the TextTiling and U00 algorithm. Some candidates for segment boundaries - where the topic continues - are irrelevant. Those boundaries can be found and removed by locating gestures that stretch over the boundary candidates. This ltering improves the segmentation accuracy of text-only segmentation.

We proposed a method of collecting humorous expressions from an online community-based question-answering (CQA) corpus where some users post a variety of questions and other users post relevant answers. Although the service is created for the purpose of knowledge exchange, there are users who enjoy posting humorous responses. Therefore, the corpus contains many interesting humour communication examples that might be useful in understanding the nature of online communications and variations in humour. Considering the size of 3; 116; 009 topics, it is necessary to introduce automation in the collection process. However, due to the context dependency of humour expressions, it is hard to collect them automatically by using keywords or key phrases. Our method uses natural language processing based on dissimilarity criteria between answer texts. By using this method, we can collect humour expressions more efficiently than by manual exploration: 30 times more examples per hour.